On a data science sabbatical in NYC - and beyond

What to learn when, and why

A forward-looking story, with hindsight

In a previous century, towards the end of the eighties, I got interested in computers
thanks to WordPerfect. This word processor helped
me write my first master’s thesis - which, by the way, had nothing to do with computing.
Had I been born a few years sooner, I would surely have used a typewriter instead.

In the early nineties, I learnt my first programming languages: Pascal, Lisp and Prolog.
If you’ve never heard of these, that’s fine. Today I hardly ever use them anymore.

Forward-looking “fact”, with hindsight: without WordPerfect, I would never have tried LaTeX. Nor SGML, HTML or XML, for that matter.
And without Pascal, Lisp and Prolog, I would never have learnt and used Perl in the nineties,
Java in the noughties or Python in this decade as my respective main programming language.

On the business side, a similar knowledge investment path is discernible in my career - it just started ten years later.
In 2000, the Lernout & Hauspie works council presented its members with balance sheets and profit & loss statements, but I was unable to read them. [Not that they were accurate, but that is another story.]
So two years later, to bridge the knowledge gap, I found myself doing an MBA at Vlerick. Which, two more years later,
made me decide to start as a self-employed consultant in voice-driven dialog systems.

With the hindsight of time and experience, these and other seemingly random knowledge investments do display
some internal logic.
In these times where the half-life of knowledge is getting shorter and shorter, it is more
important than ever to make conscious investment choices.

So if you’re faced with the choice to spend your precious time, energy and money on an time-tested, mainstream
and/or brand new technology, here’s some advice.

Guideline 1: Know your (local) industry

Even though knowledge and skills are easier to transmit and learn than ever, different industries and regions do move
at different speeds. In a Silicon Valley blockchain startup, or in a top-notch academic lab,
you’re more likely to produce and share new knowledge and software code, than to “simply” consume it. Conversely,
in a traditional retail company in Belgium, it’s definitely possible to be innovative while using
not-so-new technologies.

Both options are valid, as long as you know the league you’re playing in. The advantage of playing is a (s)lower league
is that, all other things being equal, introducing a new technology comes with a lower (technological) risk.

Don’t drive a bicycle on a highway; likewise, don’t drive a Formula 1 car on a local road. Be in sync with your local industry’s
pace of innovation.

Guideline 2: Know yourself

Are you an early adopter, or would you rather take a more conservative approach? Both are fine,
as long as you know what gives you energy, and what pays your - and your employer’s - bills.

Easily bored innovators may adopt a serial approach and happily jump from one novel technology to the next.
However, if overstretched and out-of-sync, these fast-moving innovators may not stay around long enough to see their
innovations make it into production. Which somehow defeats the purpose.

The option at the other extreme is to learn, master and apply a winning technology from its cradle to its grave.
In large and (supposedly?) stable industries and companies,
many people make a living out of supporting tried and tested technologies.
Only to discover one day that they’ve been feeding a dinosaur.

Both personal strategies are fine and come with their own risks. Just know what you’re doing and learning, and why.

Guideline 3: Diversify

A particularly effective and robust knowledge investment strategy can be to ride the technology
hype cycle simultaneously at different points of the curve. Just as in a financial portfolio, it never hurts
to spread knowledge investment risk
across multiple technologies: pick a stable one from the plateau of productivity to pay for today’s bills, and a few smaller but
more risky ones from the steeper and more slippery slopes of the curve, to pay for tomorrow’s bills.

Secondly, on a wider scale, also balance your investments according to their
expected payback time. Knowledge decay may be accelerating on average,
but an investment in an MBA is still more time-tested than the cost of learning the newest
web development stack. Mix infrequent long-term investments (personal examples: MA, MSc, MBA, data science bootcamp)
with more frequent short-lived investments
(recent examples: MOOCs on Google Cloud Platform and on Deep Learning).

Combining short and longer investment cycles significantly reduces the risk of falling through the professional cracks when an
industry or company gets disrupted. In the best scenario, you may even get a chance to join the disruptor on its path
to becoming the next incumbent.

Guideline 4: Show, don’t tell

Are you willing to blindly link your professional fate to that of a traditional, incumbent company? If not, you should
at all times be willing and able to leave tomorrow, so to speak. To reach that level of agility and freedom, never stop investing
in your knowledge. But more is needed.

Money is a convention based on trust, and so is knowledge. Anyone can be an expert, on the condition that others are willing to
recognize it - most notably by paying for the expertise. To reach that level of trust, it never hurts to demonstrate
newly acquired knowledge and skills through code, blogs, videos or any other tangible means visible to the outside world.

Each new technology offers its learners the opportunity to move through the ranks from apprentice over practitioner to
master and trainer. Over the last years, GitHub, Coursera and the like
have often been more credible sources of trust than (expensively paid for) professional certifications from established technology
vendors. The latter have an obvious conflict of interest, at least to some extent.

Conclusion

To invest wisely in your career, create and actively manage your personal knowledge portfolio. Know your industry
and yourself, so you can make your personal choices in a professional context that is willing to provide a return
for your past, current and future investments in a time horizon that suits you.
In the mean time, keep on acquiring new knowledge so as to build up agility
and resilience against professional earthquakes that can happen at any time. Diversification by payback
time, hype cycle position and personal risk profile allows for a balanced and therefore robust portfolio.